Incremental non-chronological synchronization of namespaces
    31.
    发明授权
    Incremental non-chronological synchronization of namespaces 有权
    增量的非时间同步命名空间

    公开(公告)号:US07584219B2

    公开(公告)日:2009-09-01

    申请号:US10669866

    申请日:2003-09-24

    Abstract: Described are mechanisms and techniques for enabling incremental non-chronological synchronization of namespaces. In an environment, entities must have unique names within a namespace and entities may only refer to entities that actually exist within the namespace. Synchronizing two such namespaces involves providing a mechanism for indicating that an entity has been created because a reference to that entity has been made even though that entity does not yet exist. At such time as the entity is formally created, the indication is removed. Synchronizing two such namespaces also involves providing a mechanism for indicating that an entity's unique name in the namespace has been compromised through the synchronization process.

    Abstract translation: 描述了用于实现命名空间的增量非时间同步的机制和技术。 在一个环境中,实体必须在命名空间中具有唯一的名称,实体只能引用在命名空间中实际存在的实体。 同步两个这样的命名空间涉及提供用于指示实体已经被创建的机制,因为即使该实体不存在也已经创建了对该实体的引用。 在实体正式创建的时候,指示被移除。 同步两个这样的命名空间还涉及提供一种机制,用于指示实体在命名空间中的唯一名称已经通过同步过程进行了泄露。

    Method for Asymmetric Hydrosilylation of Ketones
    32.
    发明申请
    Method for Asymmetric Hydrosilylation of Ketones 有权
    酮的不对称氢化硅烷化方法

    公开(公告)号:US20080269490A1

    公开(公告)日:2008-10-30

    申请号:US11816139

    申请日:2006-02-16

    Abstract: Method of asymmetrically hydrosilylating substrates using catalysts having a ligand of the compound of the formula (I) wherein R is optionally substituted alkyl, cycloalkyl, aryl or heteroaryl; R′ is hydrogen, optionally substituted lower alkyl; and R″ is hydrogen, halogen, optionally substituted alkyl, hydroxy, amino (e.g., primary, secondary or tertiary), alkenyl; or an enantiomer thereof; or an enantiomeric mixture thereof with a transition metal. Particularly suitable reactions include the asymmetric hydrosilylation of ketones.

    Abstract translation: 使用具有式(I)化合物的配体的催化剂的不对称氢化硅烷化方法,其中R是任选取代的烷基,环烷基,芳基或杂芳基; R'是氢,任选取代的低级烷基; 和R“是氢,卤素,任选取代的烷基,羟基,氨基(例如伯,仲或叔),烯基; 或其对映体; 或其与过渡金属的对映体混合物。 特别合适的反应包括酮的不对称氢化硅烷化。

    CHIRAL TERTIARY AMINOALKYLNAPHTHOLS

    公开(公告)号:US20080255356A1

    公开(公告)日:2008-10-16

    申请号:US12133551

    申请日:2008-06-05

    Abstract: The present invention provides bipyrimidinyl diphosphine compounds of the formula wherein R is optionally substituted alkyl, cycloalkyl, aryl or heteroaryl; R′ and R″ are independently optionally substituted alkyl, cycloalkyl, aryl or heteroaryl; or an enantiomer thereof; or an enantiomeric mixture thereof. The compounds of the formula (I) are chiral atropisomeric bipyrimidinyl diphosphine compounds and, thus, may be employed as ligands to generate chiral transition metal catalysts which may be applied in a variety of asymmetric reactions, e.g., in palladium catalyzed asymmetric allylic substitution reactions. The compounds of the present invention are easily accessible in high enantiomeric purity according to the methods disclosed herein.

    Abstract translation: 本发明提供下式的二嘧啶基二膦化合物其中R是任选取代的烷基,环烷基,芳基或杂芳基; R'和R“独立地是任选取代的烷基,环烷基,芳基或杂芳基; 或其对映体; 或其对映体混合物。 式(I)的化合物是手性的不对称双嘧啶基二膦化合物,因此可以用作配体以产生手性过渡金属催化剂,其可以用于各种不对称反应,例如钯催化的不对称烯丙基取代反应。 根据本文公开的方法,本发明的化合物可以以高对映体纯度容易地获得。

    Method of identifying image frames suitable for printing
    37.
    发明授权
    Method of identifying image frames suitable for printing 失效
    识别适合打印的图像帧的方法

    公开(公告)号:US06724937B1

    公开(公告)日:2004-04-20

    申请号:US09334177

    申请日:1999-06-16

    CPC classification number: G06T7/41

    Abstract: A method for identifying one or more image frames (100) from a series (104) of video image frames, in order to print, store or flag the selected frames. The method calculates a gradient value for each image frame in a video shot (102) series, identifies one or more gradient value peaks (324) in a corresponding range of a raw data series (302), and selects the image frames (110) corresponding to the identified gradient value peaks. Two types of frames (202, 206) are identified as being suitable for printing. A first type (202) comprises frames with a high gradient content, where the image is sharply defined. A second type of frame (206) also has good focus, but is representative of periods of relative stillness, or lack of motion between consecutive image frames (100) during the video shot series (104).

    Abstract translation: 一种用于从视频图像帧的系列(104)中识别一个或多个图像帧(100)的方法,以便打印,存储或标记所选择的帧。 该方法为视频拍摄(102)系列中的每个图像帧计算梯度值,识别原始数据序列(302)的相应范围中的一个或多个梯度值峰值(324),并选择图像帧(110) 对应于所识别的梯度值峰值。 两种类型的帧(202,206)被识别为适于打印。 第一类型(202)包括具有高梯度内容的帧,其中图像被清晰地定义。 第二类型的帧(206)也具有良好的焦点,但是代表在视频拍摄序列(104)期间连续的图像帧(100)之间的相对静止的周期或缺乏运动。

    Method and apparatus for computing the similarity between images
    38.
    发明授权
    Method and apparatus for computing the similarity between images 失效
    用于计算图像之间相似度的方法和装置

    公开(公告)号:US06718063B1

    公开(公告)日:2004-04-06

    申请号:US09458063

    申请日:1999-12-10

    CPC classification number: G06K9/00697 G06K9/469 Y10S707/99936

    Abstract: The method first segments both images into homogeneous regions (205A) and assigns (207A) semantic labels (such as “sky”, “cloud”, “water”, “foliage” etc) to the homogeneous regions to describe the content of the regions using a probabilistic method. This process also results in each assigned label for a region having an associated probability value expressing the confidence level of the label being correctly assigned The method then computes (108) a distance metric which averages over all corresponding pixels in the two images a value which is the product of a predetermined semantic difference between the assigned labels at the corresponding pixels and a weighting function which is derived from the associated probability values of the labels for each of the corresponding pixels. The semantic difference reflects similarities between the labels. For example, the semantic difference of the label pair “sky” and “foliage” is higher than the semantic difference between the more similar “sky” and “cloud” label pair. The method then compares (110) the distance metric value with a predetermined threshold value in order to determine the similarity of the images.

    Abstract translation: 该方法首先将两个图像分割成均匀区域(205A),并将(207A)语义标签(例如“天空”,“云”,“水”,“叶子等”)分配给均匀区域以描述区域的内容 使用概率方法,该过程还导致具有表示标签的置信水平正确分配的相关联概率值的区域的每个分配的标签。然后,该方法计算(108)在两者中的所有相应像素上平均的距离度量 将相应像素上分配的标签之间的预定语义差异的乘积和从每个相应像素的标签的相关概率值导出的加权函数的乘积映射成一个值,语义差异反映了标签之间的相似性 例如,标签对“天空”和“叶子”的语义差异高于更相似的“天空”之间的语义差异, 和“云”标签对。 然后,该方法将距离度量值与预定阈值进行比较(110),以便确定图像的相似性。

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